Forecasting future electricity demand is one of the most important areas
in electrical engineering, due to its vital role for secure and profitable
operations in power systems. In recent years, the advent of new concepts and
technologies such as deregulation of electricity market, smart grids, electric
cars and renewable energy integration have introduced great challenges for
power system managers and consequently, the field of electric load forecasting
becomes more and more important. This paper describes the use of an adaptive
neuro-fuzzy inference system approach for daily load curve prediction. The
methodology we propose uses univariate modeling to recognize the daily and
weekly patterns of the electric load time series as a basis for the forecast.
Results from real-world case study based on the electricity demand data in
France are presented in order to illustrate the proficiency of the proposed
approach. With an average mean absolute percentage error of 2.087%, the
effectiveness of the proposed model is clearly revealed.
Electric load forecasting, Adaptive neuro-fuzzy inference system, Soft computing, Graphical user interface
|Yayımlanma Tarihi||30 Eylül 2018|
|Yayınlandığı Sayı||Yıl 2018 Cilt: 2 Sayı: 3|
Journal of Energy Systems is the official journal of
European Conference on Renewable Energy Systems (ECRES) and
Electrical and Computer Engineering Research Group (ECERG)
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